Chapter 9 - Advanced Feature Engineering Techniques for Fraud Analytics

Published: 5 September 2023| Version 1 | DOI: 10.17632/v7r3dsgtmz.1


Dataset_1: The dataset consists of transaction timestamps (in hours) for a sample of online banking transactions. The timestamps represent the time of day when the transactions occurred. Dataset_2: The dataset, encapsulated as a pandas DataFrame "trans_David", chronicles the transactional activities of an individual named David. A salient column, "channel_cd", signifies the payment channel employed by David for each transaction. The dataset encompasses 40 entries across 14 columns, with 'channel_cd' being the focal point for the derivation of the 'freq_channel' feature.


Steps to reproduce

Dataset_1: Context: An online bank seeks to identify patterns in its transaction data to detect potential fraud due to a recent spike in fraudulent activities. Objectives: -Analyze transaction timestamps for patterns related to fraud. -Visualize transaction timings. -Identify suspicious transactions deviating from typical patterns. -Data: Transaction timestamps (in hours) for a sample of online banking activities. Analysis: -Visualize transaction times using a circular histogram. -Identify central tendencies in transaction times. -Use the von Mises distribution to analyze the data. -Identify potentially fraudulent transactions outside the expected range. Dataset_2: Data Acquisition: The dataset is simulated data, created using Python, to mimic real-world transactional patterns without using real user data. Context: Identifying the frequency of specific events, like using a payment channel, is crucial in fraud detection. Objectives: -Explore the transactional dataset. -Generate a statistical summary. -Visualize missing data. -Create a frequency feature from "channel_cd". -Dataset: A pandas DataFrame "trans_David" with transactional data of a user, David, focusing on the 'channel_cd' column. Analysis: -Data Loading and Exploration: -Load data into "trans_df". -Preview the initial and final rows. -Statistical Summary: -Generate a statistical overview. -Extract dataset details. -Visualization: -Create a heatmap to identify missing data. Feature Creation: Introduce "freq_channel" to represent the cumulative count of 'channel_cd'.


Credit Card Fraud